Ensemble Clustering for Internet Security Applications

• Published in: IEEE transactions on systems, man and cybernetics. Part C, Applications and reviews Vol. 42; no. 6; pp. 1784 - 1796
• Main Authors: Zhuang, Weiwei, Ye, Yanfang, Chen, Yong, Li, Tao
• Format: Journal Article
• Language: English
• Published: New-York, NY IEEE 01.11.2012; Institute of Electrical and Electronics Engineers
• Subjects: Applied sciences; Cluster ensemble; Clustering; Clustering algorithms; Clusters; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Data mining; Data processing. List processing. Character string processing; Exact sciences and technology; Feature extraction; Internet; Knowledge engineering; Malware; malware categorization; Mathematical models; Memory and file management (including protection and security); Memory organisation. Data processing; Phishing; phishing website detection; Security; Software; Websites; Cluster analysis; malware categorization; Fraud; Economic sciences; Aggregate model; phishing website detection; Computer virus; Efficiency; News; Selection criterion; Computer security; Pattern extraction; Computer attack; Damaging; Data analysis; Cluster; Pattern recognition; Distributed system; Consensus; Automatic measurement; Distributed algorithm; Cluster ensemble; Internet; Web site; Intrusion detection systems
• DOI: 10.1109/TSMCC.2012.2222025
• ISSN: 1094-6977

Due to their damage to Internet security, malware and phishing website detection has been the Internet security topics that are of great interests. Compared with malware attacks, phishing website fraud is a relatively new Internet crime. However, they share some common properties: 1) both malware samples and phishing websites are created at a rate of thousands per day driven by economic benefits; and 2) phishing websites represented by the term frequencies of the webpage content share similar characteristics with malware samples represented by the instruction frequencies of the program. Over the past few years, many clustering techniques have been employed for automatic malware and phishing website detection. In these techniques, the detection process is generally divided into two steps: 1) feature extraction, where representative features are extracted to capture the characteristics of the file samples or the websites; and 2) categorization, where intelligent techniques are used to automatically group the file samples or websites into different classes based on computational analysis of the feature representations. However, few have been applied in real industry products. In this paper, we develop an automatic categorization system to automatically group phishing websites or malware samples using a cluster ensemble by aggregating the clustering solutions that are generated by different base clustering algorithms. We propose a principled cluster ensemble framework to combine individual clustering solutions that are based on the consensus partition, which can not only be applied for malware categorization, but also for phishing website clustering. In addition, the domain knowledge in the form of sample-level/website-level constraints can be naturally incorporated into the ensemble framework. The case studies on large and real daily phishing websites and malware collection from the Kingsoft Internet Security Laboratory demonstrate the effectiveness and efficiency of our proposed method.